Robust detection of humans in images is important for many applications, such as visual surveillance, smart rooms, and driver assistance systems. While considerable progress has been made in detecting humans, locating multiple, possibly inter-occluded humans from static images remains a challenge.
Reliably tracking humans in videos is important for many applications, such as visual surveillance, and human computer interaction. There are a number of difficulties in performing this task. One difficulty is that the objects to be tracked need to be detected. While such detection is not difficult for moving, isolated humans viewed with a fixed camera and under fixed or slowly varying illumination, reliably detecting humans becomes a difficult problem in the presence of multiple humans with inter-object occlusion and/or a moving camera. Another difficulty is that the humans need to be tracked across the different frames with varying amounts of inter-object or scene occlusions. The image appearance of the objects changes not only with the changing viewpoints but even more strongly with the visible parts of the body and clothing. Further, it is more likely that the identities of objects may be switched during tracking when humans are close to each other.
Methods and systems for reliably detecting and tracking multiple, partially occluded humans are desired.